Motivated by the recent outbreak of coronavirus (COVID-19), we propose a stochastic model of epidemic temporal growth and mitigation based on a time-modulated Hawkes process. The model is sufficiently rich to incorporate specific characteristics of the novel coronavirus, to capture the impact of undetected, asymptomatic and super-diffusive individuals, and especially to take into account time-varying counter-measures and detection efforts. Yet, it is simple enough to allow scalable and efficient computation of the temporal evolution of the epidemic, and exploration of what-if scenarios. Compared to traditional compartmental models, our approach allows a more faithful description of virus specific features, such as distributions for the time spent in stages, which is crucial when the time-scale of control (e.g., mobility restrictions) is comparable to the lifetime of a single infection. We apply the model to the first and second wave of COVID-19 in Italy, shedding light onto several effects related to mobility restrictions introduced by the government, and to the effectiveness of contact tracing and mass testing performed by the national health service.We establish a quantitative relationship between photometric and spectroscopic detections of solar-like oscillations using ab initio, 3D, hydrodynamical numerical simulations of stellar atmospheres. We present a theoretical derivation as a proof of concept for our method. We perform realistic spectral line formation calculations to quantify the ratio between luminosity and radial velocity amplitude for two case studies the Sun and the red giant ? Tau. Luminosity amplitudes are computed based on the bolometric flux predicted by 3D simulations with granulation background modelled the same way as asteroseismic observations. Radial velocity amplitudes are determined from the wavelength shift of synthesized spectral lines with methods closely resembling those used in Birmingham Solar Oscillations Network (BiSON) and Stellar Oscillations Network Group (SONG) observations. Consequently, the theoretical luminosity to radial velocity amplitude ratios are directly comparable with corresponding observations. For the Sun, we predict theoretical ratios of 21.0 and 23.7 ppm?[m?s-1]-1 from BiSON and SONG, respectively, in good agreement with observations 19.1 and 21.6 ppm?[m?s-1]-1. https://www.selleckchem.com/products/mk-4827.html For ? Tau, we predict K2 and SONG ratios of 48.4 ppm?[m?s-1]-1, again in good agreement with observations 42.2 ppm?[m?s-1]-1, and much improved over the result from conventional empirical scaling relations that give 23.2 ppm?[m?s-1]-1. This study thus opens the path towards a quantitative understanding of solar-like oscillations, via detailed modelling of 3D stellar atmospheres.We recompute the 26-yr weekly Geocentre Motion (GCM) time-series from 1994 to 2020 through the network shift approach using Satellite Laser Ranging (SLR) observations to LAGEOS1/2. Then the Singular Spectrum Analysis (SSA) is applied for the first time to separate and investigate the geophysical signals from the GCM time-series. The Principal Components (PCs) of the embedded covariance matrix of SSA from the GCM time-series are determined based on the w-correlation criterion and two PCs with large w-correlation are regarded as one periodic signal pair. The results indicate that the annual signal in all three coordinate components and semi-annual signal in both X and Z components are detected. The annual signal from this study agrees well in both amplitude and phase with those derived by the Astronomical Institute of the University of Bern and the Center for Space Research, especially for the Y and Z components. Besides, the other periodic signals with the periods of (1043.6, 85, 28), (570, 280, 222.7) and (14.1, 15.3) days are also quantitatively explored for the first time from the GCM time-series by using SSA, interpreting the corresponding geophysical and astrodynamic sources of aliasing effects of K1/O1, T2 and Mm tides, draconitic effects, and overlapping effects of the ground-track repeatability of LAGEOS1/2.The Piscine Stream Community Estimation System (PiSCES) provides users with a hypothesized fish community for any stream reach in the conterminous United States using information obtained from Nature Serve, the US Geological Survey (USGS), StreamCat, and the Peterson Field Guide to Freshwater Fishes of North America for over 1000 native and non-native freshwater fish species. PiSCES can filter HUC8-based fish assemblages based on species-specific occurrence models; create a community abundance/biomass distribution by relating relative abundance to mean body weight of each species; and allow users to query its database to see ancillary characteristics of each species (e.g., habitat preferences and maximum size). Future efforts will aim to improve the accuracy of the species distribution database and refine/augment increase the occurrence models. The PiSCES tool is accessible at the EPA's Quantitative Environmental Domain (QED) website at https//qed.epacdx.net/pisces/.Digital Transformation (DT) has become the core motivator for almost all organizations worldwide. In order to cope up with the new demands, Higher Education Institutions (HEIs) are also giving due consideration to digitizing their services, including pedagogical services. Many challenges are being faced during the successful adoption of DT strategies and plans. One of the main obstacles is the set of challenges related to the stakeholders in HEIs; more precisely, instructors and students. This paper extracts, synthesizes, categorizes, and prioritizes the challenges hindering the success of DT in Saudi universities. Firstly, the paper extracts the main challenges faced by instructors and students, and then constructs a model of the challenges based on the tripartite classification of attitude. The paper adopts a Multi-Criteria Decision Making (MCDM) Method, called the Analytic Network Process (ANP), for the purpose of gathering instructors' and students' evaluations and prioritizing their challenges accordingly. A total of 25 instructors and students were recruited from various HEIs in Saudi Arabia to evaluate the model. The results show that learning performance, lack of access to resources, and fear of change are the most significant factors hindering students towards successful adoption of DT. On the other hand, fear of change followed by lack of experience and privacy concerns are the most significant factors hindering instructors towards successful adoption of DT. The research is intended to enlighten decision-makers in Saudi HEIs to consider non-technical challenges while planning for digitizing HEIs services.